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A Dual-Camera Edge Sensing Framework with Zone-Aware Multi-Object Tracking for Sensorless Smart Vending Cabinets

Abror Shavkatovich BuriboevDepartment of Artificial Intelligence, Gachon University, Seongnam-si 13120, Republic of KoreaFarkhat RajabovDepartment of Computer Systems, Tashkent University of Information Technologies, Tashkent 100084, UzbekistanShavkat BuriboevDepartment of Construction Engineering, Samarkand State Technical University, Samarkand 140100, UzbekistanRustem AllanyazovDepartment of Computer Systems, Tashkent University of Information Technologies, Tashkent 100084, UzbekistanGiyosjon SharipovNamangan Engineering Pedagogical InstituteAbbos AbduvaytovDepartment of Information Technologies, Samarkand Institute of Economics and Service, Samarkand 140100, UzbekistanAziza AkhmedovaDepartment of Exact Sciences, Kimyo International University in Tashkent, Tashkent 100121, UzbekistanRuzimboy SobirovDepartment of Digital Technologies and Artificial Intelligence, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent 100000, UzbekistanSu-Mi ShinData Service Center, Korea Institute of Science and Technology Information, Seoul 02456, Republic of KoreaCheolwon LeeDepartment of Computer Engineering, AI Convergence Research Center, Konkuk University, Chungju 27478, Republic of KoreaHeung Seok JeonDepartment of Computer Engineering, AI Convergence Research Center, Konkuk University, Chungju 27478, Republic of Korea

2026en

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ABI

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Top-loading smart vending cabinets require precise transaction-level product detection under strict hardware and deployment constraints. In this paper, “sensorless” refers specifically to the absence of auxiliary product-level sensing hardware, such as RFID tags, weight sensors, shelf load cells, or product-slot instrumentation; the system still uses two camera sensors. Conventional snapshot-difference methods compare only a small number of frames at the beginning and end of a transaction and therefore cannot explicitly represent intermediate product motion, such as pickup, return, inspection, occlusion, and shelf resettling. This paper proposes ZAB-Fusion, a dual-camera edge sensing framework with zone-aware multi-object tracking for sensorless smart vending cabinets. The framework combines a YOLO11-seg and RT-DETR detection ensemble with ByteTrack temporal association, projects product tracks into a three-zone vertical cabinet model, interprets compressed zone sequences using a finite-state event classifier, and integrates camera-specific event streams through an evidence-gated cross-camera fusion rule. The proposed method was evaluated on 220 in-service vending transactions containing 227 ground-truth TAKEN events and 87 RETURNED events across seven product classes. Compared with the snapshot-difference baseline, ZAB-Fusion improved recall from 0.665 to 0.925 and F1-score from 0.780 to 0.944, while maintaining a high precision of 0.963. At the transaction level, exact receipt accuracy increased from 0.645 to 0.900. Runtime analysis on an Intel N100 CPU-only edge device showed an average processing latency of 562 ms per transaction under the selected-frame inference protocol. The zone classification, finite-state event interpretation, and evidence-gated cross-camera fusion stages required only 3 ms in total. These results demonstrate that explicit motion semantics and auditable cross-camera evidence gating can improve sensorless retail transaction level recognition in sensorless smart vending cabinets without adding auxiliary product-level sensing hardware.

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DOI: 10.3390/s26165213

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